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A comment on A tutorial on the free-energy framework for modelling perception and learning.

Re:A tutorial on the free-energy framework for modelling per... by boltzmannbrain on Friday July 06, 2018 @07:25PM (#17475874)
For the probabilistic graphical model and belief prop perspective, check out Friston et al. (2017) "The graphical brain: Belief propagation and active inference": https://www.mitpressjournals.org/doi/pdf/10.1162/netn_a_0001...

For the neural corollaries of predictive coding, check out Shipp (2016) "Neural Elements for Predictive Coding": https://www.frontiersin.org/articles/10.3389/fpsyg.2016.0179...

For a state-of-art CV framework that fits with the free energy principle, check out the Recursive Cortical Network from George et al. (2017) "A generative vision model that trains with high data efficiency and breaks text-based CAPTCHAs ": http://science.sciencemag.org/content/358/6368/eaag2612

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Re:A tutorial on the free-energy framework for modelling per... by rm2040 on Saturday July 07, 2018 @02:57AM (#17477435)
How does RCN model uncertainty?
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Re:A tutorial on the free-energy framework for modelling per... by boltzmannbrain on Saturday July 07, 2018 @10:19AM (#17478562)
Max-product BP inference propagates local uncertainties in the model to arrive at a globally coherent solution. An example of where this is particularly useful is resolving border-ownership amongst neurons representing a common contour.
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